Joint Inference of Repeated Evolutionary Trajectories and Patterns of Clonal Exclusivity or Co-occurrence from Tumor Mutation Trees

Cancer progression is an evolutionary process shaped by both deterministic and stochastic forces. Multi-region and single-cell sequencing of tumors empower high-resolution reconstruction of the mutational history of each tumor. At the same time, it also highlights the extensive diversity across tumors and patients. Resolving the interactions among mutations and recovering the recurrent evolutionary processes may offer greater opportunities for successful therapeutic strategies. To this end, we present a novel probabilistic framework, called TreeMHN, for joint inference of repeated evolutionary trajectories and patterns of clonal exclusivity or co-occurrence from a cohort of intra-tumor phylogenetic trees. Through simulation studies, we show that TreeMHN outperforms existing alternatives that can only focus on one aspect of the task. By applying our method to an acute myeloid leukemia dataset, we find the most likely evolutionary trajectories and mutational patterns, consistent with and enriching known findings.